code: def edge_attention(self, edges):
      # edge UDF for equation (2)
      z2 = torch.cat([edges.src["z"], edges.dst["z"]], dim=1)
      a = self.attn_fc(z2)
      return {"e": F.leaky_relu(a)}
  
imports: import os
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn.pytorch import GATConv
  import networkx
  from dgl import DGLGraph
  from dgl.data import citation_graph
  import time
  import numpy
label_desc: Use image node_id 3 for calling the GATLayer obj's underlying member method code with example usage: obj.edge_attention(edges) and returns: dict